Watermarking technique for wireless multimedia sensor networks: A state of the art

Wireless multimedia sensor networks (WMSNs) are an emerging type of sensor network which contain sensor nodes equipped with microphones, cameras, and other sensors that produce multimedia content. These networks have the potential to enable a large class of applications ranging from military to modern healthcare. Multimedia nodes are susceptible to various types of attack, such as cropping, compression, or even physical capture and sensor replacement. Hence, security becomes an important issue in WMSNs. However, given the fact that sensors are resource constrained, the traditional intensive security algorithms are not well suited for WMSNs. This makes the traditional security techniques, based on data encryption, not very suitable for WMSNs. Watermarking techniques are usually computationally lightweight and do not require much memory resources. These techniques are being considered as an attractive alternative to the traditional techniques, because of their light resource requirements. The objective of this paper is to present a critical analysis of the existing state-of-the-art watermarking algorithms developed for WMSNs

A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at a faster pace and lower cost, based on user-provided prompts. Despite the recent significant progress in AIGC, security, privacy, ethical, and legal challenges still need to be addressed. This paper presents an in-depth survey of working principles, security and privacy threats, state-of-the-art solutions, and future challenges of the AIGC paradigm. Specifically, we first explore the enabling technologies, general architecture of AIGC, and discuss its working modes and key characteristics. Then, we investigate the taxonomy of security and privacy threats to AIGC and highlight the ethical and societal implications of GPT and AIGC technologies. Furthermore, we review the state-of-the-art AIGC watermarking approaches for regulatable AIGC paradigms regarding the AIGC model and its produced content. Finally, we identify future challenges and open research directions related to AIGC.Comment: 20 pages, 6 figures, 4 table

Security and Privacy on Generative Data in AIGC: A Survey

The advent of artificial intelligence-generated content (AIGC) represents a pivotal moment in the evolution of information technology. With AIGC, it can be effortless to generate high-quality data that is challenging for the public to distinguish. Nevertheless, the proliferation of generative data across cyberspace brings security and privacy issues, including privacy leakages of individuals and media forgery for fraudulent purposes. Consequently, both academia and industry begin to emphasize the trustworthiness of generative data, successively providing a series of countermeasures for security and privacy. In this survey, we systematically review the security and privacy on generative data in AIGC, particularly for the first time analyzing them from the perspective of information security properties. Specifically, we reveal the successful experiences of state-of-the-art countermeasures in terms of the foundational properties of privacy, controllability, authenticity, and compliance, respectively. Finally, we summarize the open challenges and potential exploration directions from each of theses properties

IPEA: the digital archive use case

Now is the time to migrate tape-based media archives to digital file-based archives for television broadcasters. These archives not only address the issue of tape-deterioration, they also create new possibilities for opening up the archive. However, the switch from tape-based to file-based is something only the very big television broadcasters can manage individually. Outer- broadcasters should work together to accomplish this task. In the Flemish part of Belgium, the two largest broadcasters in Flanders, namely the commercial broadcaster VMMa and the public broadcaster VRT, the television facilities supporting company Videohouse, and different university research groups associated with the Interdisciplinary Institute for Broadband Technology joined forces and started the "Innovative Platform on Electronic Archiving" project. The goal of this project is to develop common standards for the exchange and archiving of audio-visual data. In this paper, we give a detailed overview of this project and its different research topics

A Study of Data Security on E-Governance using Steganographic Optimization Algorithms

Steganography has been used massively in numerous fields to maintain the privacy and integrity of messages transferred via the internet. The need to secure the information has augmented with the increase in e-governance usage. The wide adoption of e-governance services also opens the doors to cybercriminals for fraudulent activities in cyberspace. To deal with these cybercrimes we need optimized and advanced steganographic techniques. Various advanced optimization techniques can be applied to steganography to obtain better results for the security of information. Various optimization techniques like particle swarm optimization and genetic algorithms with cryptography can be used to protect information for e-governance services. In this study, a comprehensive review of steganographic algorithms using optimization techniques is presented. A new perspective on using this technique to protect the information for e-governance is also presented. Deep Learning might be the area that can be used to automate the steganography process in combination with other method

Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond

Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI development. The content generated by related applications, such as text, images and audio, has sparked a heated discussion. Various derived AIGC applications are also gradually entering all walks of life, bringing unimaginable impact to people's daily lives. However, the rapid development of such generative tools has also raised concerns about privacy and security issues, and even copyright issues in AIGC. We note that advanced technologies such as blockchain and privacy computing can be combined with AIGC tools, but no work has yet been done to investigate their relevance and prospect in a systematic and detailed way. Therefore it is necessary to investigate how they can be used to protect the privacy and security of data in AIGC by fully exploring the aforementioned technologies. In this paper, we first systematically review the concept, classification and underlying technologies of AIGC. Then, we discuss the privacy and security challenges faced by AIGC from multiple perspectives and purposefully list the countermeasures that currently exist. We hope our survey will help researchers and industry to build a more secure and robust AIGC system.Comment: 43 pages, 10 figure